(Ex-Facebook & Best-Selling Data Science Author)
February 7, 2024
At Tesla, SQL is used day-to-day for analyzing vehicle performance data for predictive maintenance and enhancing production efficiency through manufacturing process optimization. Unsurprisingly this is why Tesla asks SQL coding questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you prepare for the Tesla SQL Interview, we've curated 10 Tesla SQL interview questions to practice – can you solve them and impress Elon Musk?
Given a dataset of Tesla charging stations, we'd like to analyze the usage pattern. The dataset captures when a Tesla car starts charging, finishes charging, and the charging station used. Calculate the total charging time on each station and compare it with the previous day.
The start and end times are timestamps of when a charging session began and ended.
There are 3 cars (3001, 3002, 3003) and 2 charging stations (2001, 2002) in this data set.
In this query, we're using the window function to fetch data from the previous row. Since we partition the data by station and order it by date, the previous row represents the previous day's total charging hours for each station. Therefore, calculating the difference between the total charging hours of the current day and the previous day gives the changes in the total charging hours from the previous day for each station.
Please note the usage of the extract field, which helps in converting the interval data type (created by subtracting and ) into seconds, and for the conversion to hours we are dividing it by 3600.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
In this actual Tesla SQL Interview question, a Data Analyst was given the table called and told to "Write a SQL query that determines which parts have begun the assembly process but are not yet finished?".
The assumptions for this problem are that the table contains all parts currently in production, each at varying stages of the assembly process. The second assumption is that an unfinished part is one that lacks a .
For a full explanation on the solution, and to practice this Tesla SQL question yourself, click the image below:
The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.
You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
Tesla is interested in understanding the click-through conversion rates from viewing a digital ad to adding a product(vehicle model) to the cart. Calculate the click-through conversion rates for each ad-campaign and the respective product.
For this scenario, let's work with two tables - one named "ad_clicks" that records data about the ad-campaign through which a user clicks across to the site and the product they viewed, and a second named "add_to_carts" that records data about whether the user added the product to the cart after clicking the ad.
This query first creates two CTEs ("clicks" and "adds") to count the number of ad clicks and cart adds for each ad-campaign and product model. It then joins these two CTEs on the product_model field, and calculates the conversion rate by dividing the number of adds by the number of clicks, converting it to a percentage by multiplying by 100. The result will provide the click-through conversion rates for each ad-campaign and the respective product.
To solve another question about calculating rates, try this TikTok SQL question within DataLemur's interactive coding environment:
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
For example, say you had website visitor data for Tesla, exported from the company's Google Analytics account. In support of the web-dev team, you had to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to wanted to generate all pairs of URLs, but needed to avoid pairs where both the URLs were the same since that's not a valid pair.
The self-join query woulld like the following:
This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).
In the sales department at Tesla, there's constant monitoring of average selling price per model to analyze trends and inform decision making. Write a SQL query that shows the average selling price per Tesla car model for each year.
Here's some sample data to work with:
The resulting output should have the following columns: year, model, and average price.
Below is the SQL query for the problem:
This query first extracts the year from the sale_date. It then groups the data by the year and the model, and calculates the average price for each group. The result is a list of average selling prices per Tesla car model for each year.
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
The Tesla company maintains two tables in their database, and . The table contains information about the customers including: , , , and . The table includes: , , , and . Write a SQL query that will join these two tables and return a list of customers who have bought cars, including their names, emails, the model of the car they bought, and its color.
This SQL query uses an inner join to combine the and tables based on a common field, . In the SELECT statement, we list the fields we want to display in the result: , , , and . The resulting table will show us each customer who bought a car, the model of the car, and its color.
Because joins come up so often during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:
As a data analyst at Tesla, one of your tasks is to evaluate the efficiency of newly developed batteries. You have been given a sample dataset with each row representing one test run for a battery. The dataset contains successful charge and discharge cycles, and the energy used in each of these actions (in kWh). Your task is to write a SQL script to compute the battery performance index of each run by using the following function:
Where CHARGE is energy used to fully charge the battery, DISCHARGE is energy recovered from the battery, and DAYS is the runtime of each test in days. You've to round off the performance_index to two decimal places.
In this query, for each run ID and battery model, we calculate the performance index by first subtracting the energy discharged from the energy charged (ABS makes sure the result is non-negative), then dividing it by the square root of the number of testing days ( helps us account for the end date as well). To get the result in the desired format of two decimal places, we use the ROUND function.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for dealing with energy-related efficiency calculations.
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Tesla products and a table of Tesla customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Tesla product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Tesla product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
You are a data analyst at Tesla and the company is focused on improving the efficiency of its vehicle fleet. Based on service data, the Company would like to analyze the average distance driven and power consumed by each model of its vehicles across different years.
Tesla has two tables, and .
The table has four columns: , , and , where is a unique identifier for each vehicle.
The table has four columns: , , (in miles), and (in kilowatt hour), where is a unique identifier for each service record.
The question is, can you write a query that produces a report summarizing the average distance driven and average power consumed by each model manufactured in each year?
Here's an SQL query using PostgreSQL that would solve the problem:
This query first joins the and tables using the column that's common to both. It then groups the result by and fields. For each group, it calculates the average values for and . The resulting report is ordered by and for easy understanding.
The key to acing a Tesla SQL interview is to practice, practice, and then practice some more! In addition to solving the above Tesla SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each exercise has multiple hints, fully explained answers along with a discussion board to see how others solved it and best of all, there is an interactive coding environment so you can instantly run your SQL query and have it executed.
To prep for the Tesla SQL interview you can also be helpful to solve interview questions from other car companies like:
In case your SQL foundations are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
For the Tesla Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:
The best way to prepare for Tesla Data Science interviews is by reading Ace the Data Science Interview. The book's got: